Given the complexity and functionality of todays software, task-specific, system-suggested help could be beneficial for users. Although system-suggested help assists users in completing their tasks quickly, user response to unsolicited advice from their applications has been lukewarm. One such problem is lack of knowledge of system-suggested help about the users expertise with the task they are currently doing. This thesis examines the possibility of improving system-suggested help by adding knowledge about user expertise into the help system and eventually designing an expertise-sensitive help system. An expertise-sensitive help system would detect user expertise dynamically and regularly so that systems could recommend help overtly to novices, subtly to average and poor users, and not at all to experts.<p> This thesis makes several advances in this area through a series of four experiments. In the first experiment, we show that users respond differently to help interruptions depending on their expertise with a task. Having established that user response to helpful interruptions varies with expertise level, in the second experiment we create a four-level classifier of task expertise with an accuracy of 90%. To present helpful interruptions differently to novice, poor, and average users, we need to design three interrupting notifications that vary in their attentional draw. In experiment three, we investigate a number of options and choose three icons. Finally, in experiment four, we integrate the expertise model and three interrupting notifications into an expertise-sensitive system-suggested help program, and investigate the user response. Together, these four experiments show that users value helpful interruptions when their expertise with a task is low, and that an expertise-sensitive help system that presents helpful interruptions with attentional draw that matches user expertise is effective and valuable.

Given the complexity and functionality of todays software, task-specific, system-suggested help could be beneficial for users. Although system-suggested help assists users in completing their tasks quickly, user response to unsolicited advice from their applications has been lukewarm. One such problem is lack of knowledge of system-suggested help about the users expertise with the task they are currently doing. This thesis examines the possibility of improving system-suggested help by adding knowledge about user expertise into the help system and eventually designing an expertise-sensitive help system. An expertise-sensitive help system would detect user expertise dynamically and regularly so that systems could recommend help overtly to novices, subtly to average and poor users, and not at all to experts.<p> This thesis makes several advances in this area through a series of four experiments. In the first experiment, we show that users respond differently to help interruptions depending on their expertise with a task. Having established that user response to helpful interruptions varies with expertise level, in the second experiment we create a four-level classifier of task expertise with an accuracy of 90%. To present helpful interruptions differently to novice, poor, and average users, we need to design three interrupting notifications that vary in their attentional draw. In experiment three, we investigate a number of options and choose three icons. Finally, in experiment four, we integrate the expertise model and three interrupting notifications into an expertise-sensitive system-suggested help program, and investigate the user response. Together, these four experiments show that users value helpful interruptions when their expertise with a task is low, and that an expertise-sensitive help system that presents helpful interruptions with attentional draw that matches user expertise is effective and valuable.